Wastewater monitoring for detection of public health markers during the COVID-19 pandemic: near-source monitoring of schools in England over an academic year

Hassard, Francis; Vu, Milan; Rahimzadeh, Shadi; Castro-Gutierrez, Victor; Stanton, Isobel ORCID:; Burczynska, Beata; Wildeboer, Dirk; Baio, Gianluca; Brown, Mathew R.; Garelick, Hemda; Hofman, Jan; Kasprzyk-Hordern, Barbara; Majeed, Azeem; Priest, Sally; Denise, Hubert; Khalifa, Mohammad; Bassano, Irene; Wade, Matthew J.; Grimsley, Jasmine; Lundy, Lian; Singer, Andrew C. ORCID:; Di Cesare, Mariachiara. 2023 Wastewater monitoring for detection of public health markers during the COVID-19 pandemic: near-source monitoring of schools in England over an academic year. PLoS ONE, 18 (5), e0286259. 23, pp.

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Background: Schools are high-risk settings for infectious disease transmission. Wastewater monitoring for infectious diseases has been used to identify and mitigate outbreaks in many near-source settings during the COVID-19 pandemic, including universities and hospitals but less is known about the technology when applied for school health protection. This study aimed to implement a wastewater surveillance system to detect SARS-CoV-2 and other public health markers from wastewater in schools in England. Methods: A total of 855 wastewater samples were collected from 16 schools (10 primary, 5 secondary and 1 post-16 and further education) over 10 months of school term time. Wastewater was analysed for SARS-CoV-2 genomic copies of N1 and E genes by RT-qPCR. A subset of wastewater samples was sent for genomic sequencing, enabling determination of the presence of SARS-CoV-2 and emergence of variant(s) contributing to COVID-19 infections within schools. In total, >280 microbial pathogens and >1200 AMR genes were screened using RT-qPCR and metagenomics to consider the utility of these additional targets to further inform on health threats within the schools. Results: We report on wastewater-based surveillance for COVID-19 within English primary, secondary and further education schools over a full academic year (October 2020 to July 2021). The highest positivity rate (80.4%) was observed in the week commencing 30th November 2020 during the emergence of the Alpha variant, indicating most schools contained people who were shedding the virus. There was high SARS-CoV-2 amplicon concentration (up to 9.2x106 GC/L) detected over the summer term (8th June - 6th July 2021) during Delta variant prevalence. The summer increase of SARS-CoV-2 in school wastewater was reflected in age-specific clinical COVID-19 cases. Alpha variant and Delta variant were identified in the wastewater by sequencing of samples collected from December to March and June to July, respectively. Lead/lag analysis between SARS-CoV-2 concentrations in school and WWTP data sets show a maximum correlation between the two-time series when school data are lagged by two weeks. Furthermore, wastewater sample enrichment coupled with metagenomic sequencing and rapid informatics enabled the detection of other clinically relevant viral and bacterial pathogens and AMR. Conclusions: Passive wastewater monitoring surveillance in schools can identify cases of COVID-19. Samples can be sequenced to monitor for emerging and current variants of concern at the resolution of school catchments. Wastewater based monitoring for SARS-CoV-2 is a useful tool for SARS-CoV-2 passive surveillance and could be applied for case identification and containment, and mitigation in schools and other congregate settings with high risks of transmission. Wastewater monitoring enables public health authorities to develop targeted prevention and education programmes for hygiene measures within undertested communities across a broad range of use cases.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Pollution (Science Area 2017-)
ISSN: 1932-6203
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
NORA Subject Terms: Health
Date made live: 31 May 2023 16:46 +0 (UTC)

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